Don't know what you mean by similarity score? I also don't really know what you're trying to do... but I assume you're looking for patterns and clusters. Blockmodels take the philosophy that if your network data can be compressed effectively by fitting a blockmodel, then a blockmodel is likely to be a good model for how your data were generated. In this paper <https://arxiv.org/pdf/1504.02381> Tiago explains how you can check if the time/index/sequence variable for your series of networks contains useful information. You compare the description lengths without and with that variable (Section IV). That way you could e.g. give evidence for a change point in the series. Hope this helps, Peter
On Tue, 28 Mar 2017 at 04:18 treinz <[email protected]> wrote: > > Hi Peter, > > Thanks for your reply. If I understand you correctly, what you said is > basically defining a similarity score and cluster the network into layers > and run SBM on each layer and then compare? > > Thanks, > Tim > > At 2017-03-27 06:22:18, "Peter Straka" <[email protected]> wrote: > > Do the networks have the same number of nodes? If so, you could > > - define a variable which has a distinct value for each network in > your series, > - use this variable as a layer variable > - see if this formulation is reducing overall description length, > compared to modelling each network individually. > > If description length is reduced, then the layer variable is informative > in forming the blocks. This might not be what you want if you have a time > series, though... > Peter > > On Fri, 24 Mar 2017 at 11:29 treinz <[email protected]> wrote: > > > Hi Tiago, > > Thank you for the info. Here's a follow-up question. If I have a series of > networks and I'm expecting some clusters of networks in terms of their > stochastic block structure, i.e., there exist networks that are similar to > each other when compare their block models. I'm trying to compare them and > then identify these clusters by using SBM. Is the layered SBM the > appropriate way of doing this and if so how should I use the layered SBM to > do so? I don't have enough background to fulling appreciate what's in the > paper even after I read it thoroughly and I hope you can give me some idea. > > Thanks, > Tim > > At 2017-02-24 02:39:26, "Tiago de Paula Peixoto" <[email protected]> wrote: > >On 23.02.2017 02:01, treinz wrote: > >> Hi all, > >> > >> I'm new to the graph theory field and graph-tool package. Can anyone help > >> me > >> with the following questions on SBM of layered graph: > >> > >> 1) In the example shown in > >> https://graph-tool.skewed.de/static/doc/demos/inference/inference.html#edge-layers-and-covariates, > >> the edge covariates for the Les Misérables network is passed via > >> g.ep.value: > >> > >> state = gt.minimize_blockmodel_dl(g, deg_corr=False, layers=True, > >> state_args=dict(ec=g.ep.value, > >> layers=False)) > >> > >> In this case, does the constructed layered model automatically detect how > >> many layers there should be in order to obtain a best fit SBM? If so, how > >> can one retrieve the layer membership of each edge? If not, is there a way > >> to do so in graph-tool via other function calls? > > > >Each layer corresponds to a particular value of the g.ep.value property map, > >which was passed as the `ec` parameter. There is no need to extract > >anything, since this information was provided to the function in the first > >place. > > > >> 2) There's a so called 'independent layers' model discussed in the > >> reference: Peixoto, T. P., Phys. Rev. E, 2015, 92, 042807 and it seems that > >> setting state_args=dict(ec=g.ep.value, layers=True) in the example should > >> use this model instead of the edge covariate model. But it seems from the > >> paper that on is required to input the number of layers ('C' as in Fig. 3 > >> of > >> the reference). So how exactly should I use graph-tool to use the > >> 'independent layers' model? Or is the algorithm capable of automatically > >> detecting 'C' or the number of layers from the data? > > > >The number of layers is determined automatically from the supplied `ec` > >parameter. > > > >Best, > >Tiago > > > >-- > >Tiago de Paula Peixoto <[email protected]> > > > > > > > _______________________________________________ > graph-tool mailing list > [email protected] > https://lists.skewed.de/mailman/listinfo/graph-tool > > > > > _______________________________________________ > graph-tool mailing list > [email protected] > https://lists.skewed.de/mailman/listinfo/graph-tool >
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